targetscan mir-146
Skim
2020 6 1
This report is to check target genes of mir-146a. We expected to see downregulation of mir-146a target genes in ADp40KO compare to AD. However, the analysis results isn’t conclusive to say target genes are downregulated. In here, I tested other miRNAs e.g. mir-206, let-7a and mir-1970. And I tested AD vs Ctrl, AD vs ADp40KO and Cluster 8 (DAM) vs 3 (homeostatic).
#.libPaths(c("/home/skim/R/usr_lib", .libPaths()))
#.libPaths(c(.libPaths()))
#library(Seurat, lib.loc="/data/rajewsky/shared_libs/R/")
library(Seurat)
library(tidyverse)
library(dplyr)
library(stringr)
library(ggplot2)
library(gridExtra)
library(xlsx)
library(readxl)Tested miRNAs
mir-146-5p (broadly conserved microRNA family), mir-146a-3p (other miRBase annotations)
Other control: mir-206, let-7a, mir-1970
miR-1-3p/206-3p (broadly conserved), miR-206-5p(very poorly conserved)
let-7-5p/miR-98-5p (broadly conserved), let-7-3p/miR-98-3p (very poorly conserved), let-7a-2-3p (very poorly conserved)
mir-1970 (other miRBase annotations)
miR-206 was shown to enhance LPS-induced inflammation and cause the release of amyloid-β (an essential protein in AD) in microglia ref
Let-7a is involved in maintaining microglial function in inflammation-mediated injury. During inflammation, Let-7a was shown to inhibit the production of proinflammatory mediators ref
mir-1970 is targeting Il12b
load(file = "/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/R_Scripts/20200505_pri_miRNA_dgCMatrix_Create_Seurat_with_intergenic_pri_miRNA.obj")
data9set.SO$cell_type <- data9set.SO@active.ident
mir_146_5p <- read_xlsx("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/Targetscan/TargetScan7.2__miR-146-5p.predicted_targets.xlsx")
mir_146a_3p <- read_xlsx("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/Targetscan/TargetScan7.2__miR-146a-3p.predicted_targets.xlsx")
#mir_146b_3p <- read_xlsx("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/Targetscan/TargetScan7.2__miR-146b-3p.predicted_targets.xlsx")
MG_DE_AD_Ctrl <- read.csv("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/R_Scripts/DE_csv_files/Cell_type/20200221_Cell_type_DE_by_MAST_MicrogliaAD_Ctrl.csv")
MG_DE_AD_ADp40KO <- read.csv("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/R_Scripts/DE_csv_files/Cell_type/20200221_Cell_type_DE_by_MAST_MicrogliaAD_ADp40KO.csv")
MG.marker8vs3 <- read.csv("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/R_Scripts/20200109_MG_sub_analysis.MG_marker.csv")cumulative_plot <- function(DF, is.target, target, labels){
ggplot(DF, aes(x = avg_logFC, color = is.target)) +
stat_ecdf() +
theme_classic() +
ylab("Cumulative fraction") +
xlim(-0.5, 0.5) +
scale_color_manual(values = c("black", "red"),
breaks = c(F, T),
labels = c(paste0("nontargets (", sum(!DF$is.target), ")"),
paste0(target," targets (", sum(DF$is.target), ")")),
name = "") +
theme(axis.line.y=element_line(size=.5),
axis.line.x=element_line(size=.5),
axis.text.x=element_text(size=15, family="Helvetica", color="black"),
axis.text.y=element_text(size=15, family="Helvetica", color="black"),
axis.title.x=element_text(size=15, family="Helvetica", color="black"),
axis.title.y=element_text(size=15, family="Helvetica", color="black"),
legend.text = element_text(size=10, family="Helvetica", color="black"),
legend.position = c(0.01, 1),
legend.justification = c(0, 1),
aspect.ratio=1,
plot.title=element_text(size=15, face="bold", family="Helvetica", hjust=0.5)) +
annotate("text", x = 0.2, y = 0.5, label = labels, colour = "#003366", face = "bold", size = 4, family="Helvetica")
}1. mir-146a AD vs WT
mir-146-5p Filter condition: Cumulative weighted context++ score less than -0.2
mir-146a-3p Filter condition: Cumulative weighted context++ score less than -0.3
#################
# 1. mir-146-5p
#################
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_146_5p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided")
c1 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir_146_5p", "p-value = 0.2561") + ggtitle("AD vs Ctrl in Microglia mir-146-5p")
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_146_5p[mir_146_5p$`Cumulative weighted context++ score` < -.19, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided")
c2 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir_146_5p filtered", "p-value = 0.9692") + ggtitle("AD vs Ctrl in Microglia mir-146-5p filtered")
#################
# 2. mir-146a-3p
#################
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_146a_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.0002547
c3 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir_146a_3p", "p-value = 0.0002547") + ggtitle("AD vs Ctrl in Microglia mir-146a-3p")
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_146a_3p[mir_146a_3p$`Cumulative weighted context++ score` < -.3,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") 0.1037
c4 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir_146a_3p filtered", "p-value = 0.1037") + ggtitle("AD vs Ctrl in Microglia mir-146a-3p filtered")
grid.arrange(c1, c2, c3, c4, ncol = 2)
2. mir-146a AD vs ADp40KO
#################
# 1. mir-146-5p
#################
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_146_5p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.767
c1 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir-146-5p", "p-value = 0.767") + ggtitle("AD vs ADp40KO in Microglia mir-146-5p")
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_146_5p[mir_146_5p$`Cumulative weighted context++ score` < -.19, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.5508
c2 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir-146-5p filtered", "p-value = 0.5508") + ggtitle("AD vs ADp40KO in Microglia mir-146-5p filtered")
#################
# 2. mir-146a-3p
#################
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_146a_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.002224
c3 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_146a_3p", "p-value = 0.002224") + ggtitle("AD vs ADp40KO in Microglia mir-146a-3p")
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_146a_3p[mir_146a_3p$`Cumulative weighted context++ score` < -.3,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.2492
c4 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_146a_3p filtered", "p-value = 0.2492") + ggtitle("AD vs ADp40KO in Microglia mir-146a-3p filtered")
grid.arrange(c1, c2, c3, c4, ncol = 2)
3. mir-146a Cluster 8 vs 3
#################
# 1. mir-146-5p
#################
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_146_5p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided")0.02178
c1 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"mir-146-5p", "p-value = 0.02178") + ggtitle("Cluster 8 vs 3 in Microglia mir-146-5p")
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_146_5p[mir_146_5p$`Cumulative weighted context++ score` < -.19, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.5685
c2 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"mir-146-5p filtered", "p-value = 0.5685") + ggtitle("Cluster 8 vs 3 in Microglia mir-146-5p filtered")
#################
# 2. mir-146a-3p
#################
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_146a_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.2707
c3 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"mir_146a_3p", "p-value = 0.2707") + ggtitle("Cluster 8 vs 3 in Microglia mir-146a-3p")
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_146a_3p[mir_146a_3p$`Cumulative weighted context++ score` < -.3,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.4797
c4 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_146a_3p filtered", "p-value = 0.4797") + ggtitle("Cluster 8 vs 3 in Microglia mir-146a-3p filtered")
grid.arrange(c1, c2, c3, c4, ncol = 2)
4. pri-mir-146a feature plots
data9set_MG.SO <- subset(data9set.SO, subset = seurat_clusters %in% c(3,8))
data9set_MG_Ctrl.SO <- subset(data9set_MG.SO, subset = sample %in% "Ctrl")
data9set_MG_AD.SO <- subset(data9set_MG.SO, subset = sample %in% "AD")
data9set_MG_ADp40KO.SO <- subset(data9set_MG.SO, subset = sample %in% "ADp40KO")
data9set_3.SO <- subset(data9set.SO, subset = seurat_clusters %in% c(3))
data9set_8.SO <- subset(data9set.SO, subset = seurat_clusters %in% c(8))
f1 <- FeaturePlot(data9set_MG_Ctrl.SO, features = "mir-146a", order = TRUE, pt.size = 1) + xlim(c(-7, 7)) + ylim(c(18,28)) + ggtitle("pri-mir-146a (WT)")
f2 <- FeaturePlot(data9set_MG_AD.SO, features = "mir-146a", order = TRUE, pt.size = 1) + xlim(c(-7, 7)) + ylim(c(18,28)) + ggtitle("pri-mir-146a (AD)")
f3 <- FeaturePlot(data9set_MG_ADp40KO.SO, features = "mir-146a", order = TRUE, pt.size = 1) + xlim(c(-7, 7)) + ylim(c(18,28)) + ggtitle("pri-mir-146a (ADp40KO)")
grid.arrange(f1, f2, f3, ncol = 3)
# extract UMI counts and calculate average per each gene
MG_Ctrl_count.df <- data.frame(GetAssayData(data9set_MG_Ctrl.SO, slot = "counts"))
MG_AD_count.df <- data.frame(GetAssayData(data9set_MG_AD.SO, slot = "counts"))
MG_ADp40KO_count.df <- data.frame(GetAssayData(data9set_MG_ADp40KO.SO, slot = "counts"))
MG_3_count.df <- data.frame(GetAssayData(data9set_3.SO, slot = "counts"))
MG_8_count.df <- data.frame(GetAssayData(data9set_8.SO, slot = "counts"))
MG_Ctrl_count_cpm.df <- apply(MG_Ctrl_count.df, 2, function(x) (x/sum(x)) * 1000000)
MG_AD_count_cpm.df <- apply(MG_AD_count.df, 2, function(x) (x/sum(x)) * 1000000)
MG_ADp40KO_count_cpm.df <- apply(MG_ADp40KO_count.df, 2, function(x) (x/sum(x)) * 1000000)
MG_3_count_cpm.df <- apply(MG_3_count.df, 2, function(x) (x/sum(x)) * 1000000)
MG_8_count_cpm.df <- apply(MG_8_count.df, 2, function(x) (x/sum(x)) * 1000000)
MG_Ctrl_count_cpm.df <- data.frame(Matrix::rowMeans(MG_Ctrl_count_cpm.df))
MG_AD_count_cpm.df <- data.frame(Matrix::rowMeans(MG_AD_count_cpm.df))
MG_ADp40KO_count_cpm.df <- data.frame(Matrix::rowMeans(MG_ADp40KO_count_cpm.df))
MG_3_count_cpm.df <- data.frame(Matrix::rowMeans(MG_3_count_cpm.df))
MG_8_count_cpm.df <- data.frame(Matrix::rowMeans(MG_8_count_cpm.df))
MG_count_cpm.df <- cbind(MG_Ctrl_count_cpm.df, MG_AD_count_cpm.df,MG_ADp40KO_count_cpm.df, MG_3_count_cpm.df, MG_8_count_cpm.df)
MG_count_cpm.df <- as.data.frame(MG_count_cpm.df)
MG_count_cpm.df$gene <- rownames(MG_count_cpm.df)
# Remove Ttr
MG_count_cpm.df <- MG_count_cpm.df[MG_count_cpm.df$gene != "Ttr", ]
MG_count_cpm.df$gene <- NULL
# remove genes where all zero in samples
MG_count_cpm.df <- MG_count_cpm.df[apply(MG_count_cpm.df == 0, 1, sum) != 5, ]
colnames(MG_count_cpm.df) <- c("WT", "AD", "ADp40KO", "Cluster3", "Cluster8")
# add pseudocount 0.001 and logarithm
MG_count_cpm.df <- log(MG_count_cpm.df + 1)
MG_count_cpm.df$gene <- rownames(MG_count_cpm.df)
ggplot(MG_count_cpm.df, aes(x = WT, y = AD)) + geom_point(size = 1, color = "#666666", alpha = 0.2) +
geom_point(data = MG_count_cpm.df[MG_count_cpm.df$gene %in% "mir-146a", ], aes(x = WT, y = AD), size = 2, color = "red") + xlim(c(0,9)) + ylim(c(0,9)) +
ggtitle("WT vs AD in Microglia") +
theme_classic() +
xlab("log (average CPM + 1) WT") +
ylab("log (average CPM + 1) AD") +
theme(plot.title = element_text(size = 15, hjust = 0.5, family = "helvetica"),
axis.title = element_text(size = 15),
axis.text = element_text(size = 12, color = "black")) +
geom_abline(slope = 1, color="red", linetype="dashed", size=.25) +
annotate("text", x = 5.5, y = 4.2, label = "pri-mir-146a", size = 4.5)
ggplot(MG_count_cpm.df, aes(x = AD, y = ADp40KO)) + geom_point(size = 1, color = "#666666", alpha = 0.2) +
geom_point(data = MG_count_cpm.df[MG_count_cpm.df$gene %in% "mir-146a", ], aes(x = AD, y = ADp40KO), size = 2, color = "red") + xlim(c(0,9)) + ylim(c(0,9)) +
ggtitle("AD vs ADp40KO in Microglia") +
theme_classic() +
xlab("log (average CPM + 1) AD") +
ylab("log (average CPM + 1) ADp40KO") +
theme(plot.title = element_text(size = 15, hjust = 0.5, family = "helvetica"),
axis.title = element_text(size = 15),
axis.text = element_text(size = 12, color = "black")) +
geom_abline(slope = 1, color="red", linetype="dashed", size=.25) +
annotate("text", x = 4.2, y = 5.8, label = "pri-mir-146a", size = 4.5)
ggplot(MG_count_cpm.df, aes(x = Cluster3, y = Cluster8)) + geom_point(size = 1, color = "#666666", alpha = 0.2) +
geom_point(data = MG_count_cpm.df[MG_count_cpm.df$gene %in% "mir-146a", ], aes(x = Cluster3, y = Cluster8), size = 2, color = "red") + xlim(c(0,9)) + ylim(c(0,9)) +
ggtitle("Cluster3 vs Cluster8 in Microglia") +
theme_classic() +
xlab("log (average CPM + 1) Cluster3") +
ylab("log (average CPM + 1) Cluster8") +
theme(plot.title = element_text(size = 15, hjust = 0.5, family = "helvetica"),
axis.title = element_text(size = 15),
axis.text = element_text(size = 12, color = "black")) +
geom_abline(slope = 1, color="red", linetype="dashed", size=.25) +
annotate("text", x = 4.2, y = 5.8, label = "pri-mir-146a", size = 4.5)
5. mir-206
miR-1-3p/206-3p (broadly conserved), miR-206-5p(very poorly conserved)
mir_206_3p <- read_xlsx("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/Targetscan/TargetScan7.2__miR-1-3p_206-3p.predicted_targets.xlsx")
mir_206_5p <- read_xlsx("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/Targetscan/TargetScan7.2__miR-206-5p.predicted_targets.xlsx")both filter Cumulative weighted context++ score` < -.3
#################
# 1. mir-206-3p
#################
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_206_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.2163
c1 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir_206-3p", "p-value = 0.2163") + ggtitle("AD vs Ctrl in Microglia mir_206-3p")
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_206_3p[mir_206_3p$`Cumulative weighted context++ score` < -.3, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.009183
c2 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir_206-3p filtered", "p-value = 0.009183") + ggtitle("AD vs Ctrl in Microglia mir_206-3p filtered")
#################
# 2. mir-206-5p
#################
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_206_5p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.02463
c3 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir-206-5p", "p-value = 0.02463") + ggtitle("AD vs Ctrl in Microglia mir-206-5p")
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_206_5p[mir_206_5p$`Cumulative weighted context++ score` < -.3,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.628
c4 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir-206-5p filtered", "p-value = 0.628") + ggtitle("AD vs Ctrl in Microglia mir-206-5p filtered")
grid.arrange(c1, c2, c3, c4, ncol = 2)
#################
# 1. mir-206-3p
#################
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_206_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.008269
c1 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_206-3p", "p-value = 0.008269") + ggtitle("AD vs ADp40KO in Microglia mir_206-3p")
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_206_3p[mir_206_3p$`Cumulative weighted context++ score` < -.3, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.94
c2 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_206-3p filtered", "p-value = 0.94") + ggtitle("AD vs ADp40KO in Microglia mir_206-3p filtered")
#################
# 2. mir-206-5p
#################
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_206_5p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.006348
c3 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_206_5p", "p-value = 0.006348") + ggtitle("AD vs ADp40KO in Microglia mir_206_5p")
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_206_5p[mir_206_5p$`Cumulative weighted context++ score` < -.3,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.5615
c4 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_206_5p filtered", "p-value = 0.5615") + ggtitle("AD vs ADp40KO in Microglia mir_206_5p filtered")
grid.arrange(c1, c2, c3, c4, ncol = 2)
#################
# 1. mir-206-3p
#################
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_206_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided")#0.2663
c1 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"mir_206_3p", "p-value = 0.2663") + ggtitle("Cluster 8 vs 3 in Microglia mir_206_3p")
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_206_3p[mir_206_3p$`Cumulative weighted context++ score` < -.3, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.7879
c2 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"mir_206_3p filtered", "p-value = 0.7879") + ggtitle("Cluster 8 vs 3 in Microglia mir_206_3p filtered")
#################
# 2. mir-206-5p
#################
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_206_5p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.8873
c3 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"mir_206_5p", "p-value = 0.8873") + ggtitle("Cluster 8 vs 3 in Microglia mir_206_5p")
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_206_5p[mir_206_5p$`Cumulative weighted context++ score` < -.3,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.3428
c4 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_206_5p filtered", "p-value = 0.3428") + ggtitle("Cluster 8 vs 3 in Microglia mir_206_5p filtered")
grid.arrange(c1, c2, c3, c4, ncol = 2)
6. let-7a
let-7-5p/miR-98-5p (broadly conserved), let-7-3p/miR-98-3p (very poorly conserved), let-7a-2-3p (very poorly conserved)
let_7_5p <- read_xlsx("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/Targetscan/TargetScan7.2__let-7-5p_miR-98-5p.predicted_targets.xlsx")
let_7_3p <- read_xlsx("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/Targetscan/TargetScan7.2__let-7-3p_miR-98-3p.predicted_targets.xlsx")
let_7a_2_3p <- read_xlsx("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/Targetscan/TargetScan7.2__let-7a-2-3p.predicted_targets.xlsx")let_7_5p: filter Cumulative weighted context++ score` < -.5
let_7_3p: filter Cumulative weighted context++ score` < -.3
let_7a_2_3p: filter Cumulative weighted context++ score` < -.4
#################
# 1. let_7_5p
#################
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% let_7_5p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.3167
c1 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"let_7_5p", "p-value = 0.3167") + ggtitle("AD vs Ctrl in Microglia let_7_5p")
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% let_7_5p[let_7_5p$`Cumulative weighted context++ score` < -.5, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.4581
c2 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"let_7_5p filtered", "p-value = 0.4581") + ggtitle("AD vs Ctrl in Microglia let_7_5p filtered")
#################
# 2. let-7-3p
#################
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% let_7_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #5.93e-06
c3 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"let_7_3p", "p-value = 5.93e-06") + ggtitle("AD vs Ctrl in Microglia let_7_3p")
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% let_7_3p[let_7_3p$`Cumulative weighted context++ score` < -.3,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.7751
c4 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"let_7_3p filtered", "p-value = 0.7751") + ggtitle("AD vs Ctrl in Microglia let_7_3p filtered")
#################
# 3. let_7a_2_3p
#################
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% let_7a_2_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.01047
c5 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"let_7a_2_3p", "p-value = 0.01047") + ggtitle("AD vs Ctrl in Microglia let_7a_2_3p")
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% let_7a_2_3p[let_7a_2_3p$`Cumulative weighted context++ score` < -.4,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.009217
c6 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"let_7a_2_3p filtered", "p-value = 0.009217") + ggtitle("AD vs Ctrl in Microglia let_7a_2_3p filtered")
grid.arrange(c1, c2, c3, c4, c5, c6, ncol = 2)
#################
# 1. let_7_5p
#################
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% let_7_5p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.8359
c1 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"let_7_5p", "p-value = 0.8359") + ggtitle("AD vs ADp40KO in Microglia let_7_5p")
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% let_7_5p[let_7_5p$`Cumulative weighted context++ score` < -.5, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.8822
c2 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"let_7_5p filtered", "p-value = 0.8822") + ggtitle("AD vs ADp40KO in Microglia let_7_5p filtered")
#################
# 2. let-7-3p
#################
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% let_7_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.001065
c3 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"let_7_3p", "p-value = 0.001065") + ggtitle("AD vs ADp40KO in Microglia let_7_3p")
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% let_7_3p[let_7_3p$`Cumulative weighted context++ score` < -.3,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.7597
c4 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"let_7_3p filtered", "p-value = 0.7597") + ggtitle("AD vs ADp40KO in Microglia let_7_3p filtered")
#################
# 3. let_7a_2_3p
#################
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% let_7a_2_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.002325
c5 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"let_7a_2_3p", "p-value = 0.002325") + ggtitle("AD vs ADp40KO in Microglia let_7a_2_3p")
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% let_7a_2_3p[let_7a_2_3p$`Cumulative weighted context++ score` < -.4,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.3017
c6 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"let_7a_2_3p filtered", "p-value = 0.3017") + ggtitle("AD vs ADp40KO in Microglia let_7a_2_3p filtered")
grid.arrange(c1, c2, c3, c4, c5, c6, ncol = 2)
#################
# 1. let_7_5p
#################
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% let_7_5p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.1216
c1 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"let_7_5p", "p-value = 0.1216") + ggtitle("Cluster 8 vs 3 in Microglia let_7_5p")
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% let_7_5p[let_7_5p$`Cumulative weighted context++ score` < -.5, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.77
c2 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"let_7_5p filtered", "p-value = 0.77") + ggtitle("Cluster 8 vs 3 in Microglia let_7_5p filtered")
#################
# 2. let-7-3p
#################
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% let_7_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.006998
c3 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"let_7_3p", "p-value = 0.006998") + ggtitle("Cluster 8 vs 3 in Microglia let_7_3p")
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% let_7_3p[let_7_3p$`Cumulative weighted context++ score` < -.3,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.9665
c4 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"let_7_3p filtered", "p-value = 0.9665") + ggtitle("Cluster 8 vs 3 in Microglia let_7_3p filtered")
#################
# 3. let_7a_2_3p
#################
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% let_7a_2_3p$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.2467
c5 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"let_7a_2_3p", "p-value = 0.2467") + ggtitle("Cluster 8 vs 3 in Microglia let_7a_2_3p")
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% let_7a_2_3p[let_7a_2_3p$`Cumulative weighted context++ score` < -.4,]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.02784
c6 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"let_7a_2_3p filtered", "p-value = 0.02784") + ggtitle("Cluster 8 vs 3 in Microglia let_7a_2_3p filtered")
grid.arrange(c1, c2, c3, c4, c5, c6, ncol = 2)
7. mir-1970
mir_1970 <- read_xlsx("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/miRNA_lncRNA/Targetscan/TargetScan7.2__miR-1970.predicted_targets.xlsx")filter Cumulative weighted context++ score` < -.4
#################
# 1. AD vs Ctrl
#################
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_1970$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.4299
c1 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir_1970", "p-value = 0.4299") + ggtitle("AD vs Ctrl in Microglia mir_1970")
MG_DE_AD_Ctrl$is.target <- MG_DE_AD_Ctrl$X %in% mir_1970[mir_1970$`Cumulative weighted context++ score` < -.4, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_Ctrl, alt = "two.sided") #0.7691
c2 <- cumulative_plot(MG_DE_AD_Ctrl, is.target = is.target,"mir_1970 filtered", "p-value = 0.7691") + ggtitle("AD vs Ctrl in Microglia mir_1970 filtered")
###################
# 2. AD vs ADp40KO
###################
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_1970$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #3.381e-05
c3 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_1970", "p-value = 3.381e-05") + ggtitle("AD vs ADp40KO in Microglia mir_1970")
MG_DE_AD_ADp40KO$is.target <- MG_DE_AD_ADp40KO$X %in% mir_1970[mir_1970$`Cumulative weighted context++ score` < -.4, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG_DE_AD_ADp40KO, alt = "two.sided") #0.04052
c4 <- cumulative_plot(MG_DE_AD_ADp40KO, is.target = is.target,"mir_1970 filtered", "p-value = 0.04052") + ggtitle("AD vs ADp40KO in Microglia mir_1970 filtered")
###################
# 3. Cluster 8 vs 3
###################
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_1970$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.5368
c5 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"mir_1970", "p-value = 0.5368") + ggtitle("Cluster 8 vs 3 in Microglia mir_1970")
MG.marker8vs3$is.target <- MG.marker8vs3$X %in% mir_1970[mir_1970$`Cumulative weighted context++ score` < -.4, ]$`Target gene`
#wilcox.test(avg_logFC ~ is.target, data = MG.marker8vs3, alt = "two.sided") #0.4462
c6 <- cumulative_plot(MG.marker8vs3, is.target = is.target,"mir_1970 filtered", "p-value = 0.4462") + ggtitle("Cluster 8 vs 3 in Microglia mir_1970 filtered")
grid.arrange(c1, c2, c3, c4, c5, c6, ncol = 2)
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
## [9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] readxl_1.3.1 xlsx_0.6.3 gridExtra_2.3 forcats_0.5.0
## [5] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
## [9] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.1 tidyverse_1.3.0
## [13] Seurat_3.1.2
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.0-10 Rtsne_0.15 colorspace_1.4-1
## [4] ellipsis_0.3.1 ggridges_0.5.2 fs_1.4.1
## [7] rstudioapi_0.11 farver_2.0.3 leiden_0.3.3
## [10] listenv_0.8.0 ggrepel_0.8.2 fansi_0.4.1
## [13] lubridate_1.7.9 mvtnorm_1.1-1 xml2_1.3.2
## [16] codetools_0.2-16 splines_3.6.0 R.methodsS3_1.8.0
## [19] mnormt_1.5-7 knitr_1.28 TFisher_0.2.0
## [22] jsonlite_1.6.1 rJava_0.9-12 broom_0.5.6
## [25] ica_1.0-2 cluster_2.1.0 dbplyr_1.4.4
## [28] png_0.1-7 R.oo_1.23.0 uwot_0.1.8
## [31] sctransform_0.2.1 compiler_3.6.0 httr_1.4.1
## [34] backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18
## [37] lazyeval_0.2.2 cli_2.0.2 htmltools_0.4.0
## [40] tools_3.6.0 rsvd_1.0.3 igraph_1.2.5
## [43] gtable_0.3.0 glue_1.4.1 RANN_2.6.1
## [46] reshape2_1.4.4 rappdirs_0.3.1 Rcpp_1.0.4.6
## [49] Biobase_2.46.0 cellranger_1.1.0 vctrs_0.3.1
## [52] multtest_2.42.0 ape_5.4 nlme_3.1-139
## [55] gbRd_0.4-11 lmtest_0.9-37 xfun_0.14
## [58] xlsxjars_0.6.1 globals_0.12.5 rvest_0.3.5
## [61] lifecycle_0.2.0 irlba_2.3.3 future_1.17.0
## [64] MASS_7.3-51.6 zoo_1.8-8 scales_1.1.1
## [67] hms_0.5.3 parallel_3.6.0 sandwich_2.5-1
## [70] RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.16
## [73] pbapply_1.4-2 stringi_1.4.6 mutoss_0.1-12
## [76] plotrix_3.7-8 BiocGenerics_0.32.0 bibtex_0.4.2.2
## [79] Rdpack_0.11-1 SDMTools_1.1-221.2 rlang_0.4.6
## [82] pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41
## [85] ROCR_1.0-11 labeling_0.3 htmlwidgets_1.5.1
## [88] cowplot_1.0.0 tidyselect_1.1.0 RcppAnnoy_0.0.16
## [91] plyr_1.8.6 magrittr_1.5 R6_2.4.1
## [94] generics_0.0.2 multcomp_1.4-13 DBI_1.1.0
## [97] withr_2.2.0 haven_2.3.1 pillar_1.4.4
## [100] sn_1.6-2 fitdistrplus_1.1-1 survival_3.1-12
## [103] future.apply_1.5.0 tsne_0.1-3 modelr_0.1.8
## [106] crayon_1.3.4 KernSmooth_2.23-15 plotly_4.9.2.1
## [109] rmarkdown_2.2 grid_3.6.0 data.table_1.12.8
## [112] blob_1.2.1 reprex_0.3.0 metap_1.3
## [115] digest_0.6.25 numDeriv_2016.8-1.1 R.utils_2.9.2
## [118] stats4_3.6.0 munsell_0.5.0 viridisLite_0.3.0